# Sensor Placement for Learning in Flow Networks

arXiv: https://arxiv.org/abs/2401.02438 · HTML: https://mlsanity.com/paper/2401.02438/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 35 references that are in the map.
- First posted on arXiv 12 Dec 2023.

## Builds on (most cited first, 5 of 5)

- [Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecasting](https://mlsanity.com/paper/2012.09641/) (2020)
- [Graph-based Semi-Supervised & Active Learning for Edge Flows](https://mlsanity.com/paper/1905.07451/) (2019)
- [Active Learning on Trees and Graphs](https://mlsanity.com/paper/1301.5112/) (2013)
- [Active Semi-Supervised Learning Using Sampling Theory for Graph Signals](https://mlsanity.com/paper/1405.4324/) (2014)
- [Optimal Sensor Placement for Topology Identification in Smart Power Grids](https://mlsanity.com/paper/1901.11104/) (2019)

## Abstract (arXiv)

Large infrastructure networks (e.g. for transportation and power distribution) require constant monitoring for failures, congestion, and other adversarial events. However, assigning a sensor to every link in the network is often infeasible due to placement and maintenance costs. Instead, sensors can be placed only on a few key links, and machine learning algorithms can be leveraged for the inference of missing measurements (e.g. traffic counts, power flows) across the network. This paper investigates the sensor placement problem for networks. We first formalize the problem under a flow conservation assumption and show that it is NP-hard to place a fixed set of sensors optimally. Next, we propose an efficient and adaptive greedy heuristic for sensor placement that scales to large networks. Our experiments, using datasets from real-world application domains, show that the proposed approach enables more accurate inference than existing alternatives from the literature. We demonstrate that considering even imperfect or incomplete ground-truth estimates can vastly improve the prediction error, especially when a small number of sensors is available.